In this task, you will define two additional models by using the Mining Models tab of Data Mining Designer. You will use the Microsoft Clustering and Microsoft Naive Bayes algorithms to create the models. These two algorithms are selected because of their ability to predict a discrete value (i.e., bike purchase). For more information about these algorithms, see Microsoft Clustering Algorithm and Microsoft Naive Bayes Algorithm

To create a clustering mining model

Notice that the designer displays two columns, one for the mining structure and one for the TM_Decision_Tree mining model, which you created in the previous lesson.

Right-click the Structure column and select New Mining Model.

In the New Mining Model dialog box, in Model name, type TM_Clustering.

In Algorithm name, select Microsoft Clustering.

Click OK.

The new model now appears in the Mining Models tab of Data Mining Designer. This model, built with the Microsoft Clustering algorithm, groups customers with similar characteristics into clusters and predicts bike buying for each cluster. Although you can modify the column usage and properties for the new model, no changes to the TM_Clustering model are necessary for this tutorial.

To create a Naive Bayes mining model

In the New Mining Model dialog box, under Model name, type TM_NaiveBayes.

In Algorithm name, select Microsoft Naive Bayes, then click OK.

A message appears stating that the Microsoft Naive Bayes algorithm does not support the Age and Yearly Income columns, which are continuous.

Click Yes to acknowledge the message and continue.

A new model appears in the Mining Models tab of Data Mining Designer. Although you can modify the column usage and properties for all the models in this tab, no changes to the TM_NaiveBayes model are necessary for this tutorial.